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Record W2972635937 · doi:10.1016/j.procs.2019.08.044

HoBAC: toward a Higher-order Attribute-Based Access Control Model

2019· article· en· W2972635937 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProcedia Computer Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceAccess controlGeneralizationRole-based access controlTask (project management)Computer securityControl (management)Order (exchange)Distributed computingArtificial intelligence

Abstract

fetched live from OpenAlex

Ensuring security in dynamic environments such as the Internet of Things (IoT) is a prominent task and challenging at the same time. Attribute-Based Access Control (ABAC) is one of the most promising access control models and standards which has received meaningful attention in recent years. In this paper, we propose the theoretical foundations of an access control model, named Higher-order Attribute-Based Access Control (HoBAC), that is a generalization of ABAC. HoBAC extends the basic concepts of ABAC (subjects, objects and contexts) with aggregation operations that yields hierarchies. To show the effectiveness of HoBAC, two instances of the theoretical model are presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.317
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it